Overview

Dataset statistics

Number of variables21
Number of observations832
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.3%
Total size in memory136.6 KiB
Average record size in memory168.2 B

Variable types

Numeric20
Categorical1

Alerts

Dataset has 11 (1.3%) duplicate rowsDuplicates
push is highly correlated with mov and 8 other fieldsHigh correlation
mov is highly correlated with push and 8 other fieldsHigh correlation
call is highly correlated with push and 7 other fieldsHigh correlation
sub is highly correlated with push and 6 other fieldsHigh correlation
jmp is highly correlated with push and 8 other fieldsHigh correlation
add is highly correlated with push and 6 other fieldsHigh correlation
cmp is highly correlated with push and 8 other fieldsHigh correlation
test is highly correlated with push and 9 other fieldsHigh correlation
lea is highly correlated with push and 6 other fieldsHigh correlation
pop is highly correlated with push and 8 other fieldsHigh correlation
FindFirstFile is highly correlated with GetFileAttributesWHigh correlation
SearchPathW is highly correlated with FindResourceEx and 3 other fieldsHigh correlation
SetFilePointer is highly correlated with cmpHigh correlation
FindResourceEx is highly correlated with SetFileAttributesW and 3 other fieldsHigh correlation
GetFileAttributesW is highly correlated with FindFirstFile and 2 other fieldsHigh correlation
SetFileAttributesW is highly correlated with FindResourceEx and 5 other fieldsHigh correlation
SetFilePointerEx is highly correlated with GetFileAttributesW and 2 other fieldsHigh correlation
CryptEncrypt is highly correlated with GetFileAttributesW and 2 other fieldsHigh correlation
CreateThread is highly correlated with FindResourceEx and 3 other fieldsHigh correlation
FindResourceExW is highly correlated with FindResourceEx and 3 other fieldsHigh correlation
Cerber is highly correlated with test and 4 other fieldsHigh correlation
SetFilePointer is highly skewed (γ1 = 25.04170858) Skewed
FindFirstFile has 80 (9.6%) zeros Zeros
SearchPathW has 276 (33.2%) zeros Zeros
SetFilePointer has 120 (14.4%) zeros Zeros
FindResourceEx has 196 (23.6%) zeros Zeros
GetFileAttributesW has 105 (12.6%) zeros Zeros
SetFileAttributesW has 290 (34.9%) zeros Zeros
SetFilePointerEx has 316 (38.0%) zeros Zeros
CryptEncrypt has 452 (54.3%) zeros Zeros
CreateThread has 124 (14.9%) zeros Zeros
FindResourceExW has 195 (23.4%) zeros Zeros

Reproduction

Analysis started2022-10-12 05:25:05.868830
Analysis finished2022-10-12 05:25:46.705960
Duration40.84 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

push
Real number (ℝ≥0)

HIGH CORRELATION

Distinct537
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14395.25721
Minimum0
Maximum263735
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:46.872999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile233
Q12123
median5176
Q39617
95-th percentile68582.45
Maximum263735
Range263735
Interquartile range (IQR)7494

Descriptive statistics

Standard deviation30065.39328
Coefficient of variation (CV)2.088562423
Kurtosis22.25078828
Mean14395.25721
Median Absolute Deviation (MAD)3111
Skewness4.29330906
Sum11976854
Variance903927873.1
MonotonicityNot monotonic
2022-10-12T14:25:46.971020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206523
 
2.8%
498413
 
1.6%
2669711
 
1.3%
517411
 
1.3%
915510
 
1.2%
23218
 
1.0%
51868
 
1.0%
51767
 
0.8%
52457
 
0.8%
1727
 
0.8%
Other values (527)727
87.4%
ValueCountFrequency (%)
01
 
0.1%
201
 
0.1%
451
 
0.1%
481
 
0.1%
732
0.2%
753
0.4%
781
 
0.1%
791
 
0.1%
971
 
0.1%
991
 
0.1%
ValueCountFrequency (%)
2637351
0.1%
2312901
0.1%
2214561
0.1%
2166331
0.1%
2036011
0.1%
1986421
0.1%
1915311
0.1%
1642421
0.1%
1621921
0.1%
1564671
0.1%

mov
Real number (ℝ≥0)

HIGH CORRELATION

Distinct556
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32521.14663
Minimum0
Maximum559276
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:47.067042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile505
Q12848.75
median9438
Q316795.75
95-th percentile179778.1
Maximum559276
Range559276
Interquartile range (IQR)13947

Descriptive statistics

Standard deviation67279.36319
Coefficient of variation (CV)2.068788162
Kurtosis13.84546841
Mean32521.14663
Median Absolute Deviation (MAD)6803
Skewness3.447333842
Sum27057594
Variance4526512711
MonotonicityNot monotonic
2022-10-12T14:25:47.159063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143123
 
2.8%
1043611
 
1.3%
93929
 
1.1%
94519
 
1.1%
53308
 
1.0%
227478
 
1.0%
94537
 
0.8%
53357
 
0.8%
108606
 
0.7%
3326
 
0.7%
Other values (546)738
88.7%
ValueCountFrequency (%)
01
 
0.1%
161
 
0.1%
171
 
0.1%
193
0.4%
261
 
0.1%
331
 
0.1%
401
 
0.1%
411
 
0.1%
421
 
0.1%
431
 
0.1%
ValueCountFrequency (%)
5592761
0.1%
4511981
0.1%
4244651
0.1%
4010881
0.1%
3613681
0.1%
3600891
0.1%
3430621
0.1%
3416721
0.1%
3338191
0.1%
3319501
0.1%

call
Real number (ℝ≥0)

HIGH CORRELATION

Distinct463
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7992.871394
Minimum0
Maximum176399
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:47.257085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile110
Q1924
median1129
Q33773
95-th percentile46890.6
Maximum176399
Range176399
Interquartile range (IQR)2849

Descriptive statistics

Standard deviation18127.67891
Coefficient of variation (CV)2.267980807
Kurtosis19.46832262
Mean7992.871394
Median Absolute Deviation (MAD)472
Skewness3.915587357
Sum6650069
Variance328612742.8
MonotonicityNot monotonic
2022-10-12T14:25:47.352116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112627
 
3.2%
112824
 
2.9%
90923
 
2.8%
92421
 
2.5%
157415
 
1.8%
112715
 
1.8%
97713
 
1.6%
92612
 
1.4%
112910
 
1.2%
142210
 
1.2%
Other values (453)662
79.6%
ValueCountFrequency (%)
01
 
0.1%
25
0.6%
31
 
0.1%
51
 
0.1%
61
 
0.1%
91
 
0.1%
101
 
0.1%
161
 
0.1%
171
 
0.1%
181
 
0.1%
ValueCountFrequency (%)
1763991
0.1%
1375901
0.1%
1066231
0.1%
1052791
0.1%
1041631
0.1%
1029271
0.1%
1002531
0.1%
929341
0.1%
884441
0.1%
864121
0.1%

sub
Real number (ℝ≥0)

HIGH CORRELATION

Distinct474
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3773.137019
Minimum0
Maximum135090
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:47.453128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.55
Q1236
median2035.5
Q32655.25
95-th percentile15219.6
Maximum135090
Range135090
Interquartile range (IQR)2419.25

Descriptive statistics

Standard deviation9665.534673
Coefficient of variation (CV)2.561670733
Kurtosis63.11446338
Mean3773.137019
Median Absolute Deviation (MAD)1639
Skewness6.86722807
Sum3139250
Variance93422560.51
MonotonicityNot monotonic
2022-10-12T14:25:47.557153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9532
 
3.8%
265217
 
2.0%
264911
 
1.3%
212011
 
1.3%
164910
 
1.2%
265310
 
1.2%
265110
 
1.2%
215010
 
1.2%
9310
 
1.2%
1199
 
1.1%
Other values (464)702
84.4%
ValueCountFrequency (%)
01
 
0.1%
14
0.5%
31
 
0.1%
143
0.4%
164
0.5%
171
 
0.1%
201
 
0.1%
261
 
0.1%
281
 
0.1%
302
0.2%
ValueCountFrequency (%)
1350901
0.1%
858551
0.1%
811521
0.1%
777771
0.1%
616171
0.1%
561441
0.1%
561421
0.1%
561411
0.1%
559751
0.1%
520291
0.1%

jmp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct444
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3413.992788
Minimum0
Maximum57380
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:47.659175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.65
Q1328.25
median1100
Q32877
95-th percentile17277.05
Maximum57380
Range57380
Interquartile range (IQR)2548.75

Descriptive statistics

Standard deviation6242.401192
Coefficient of variation (CV)1.828475213
Kurtosis18.11688131
Mean3413.992788
Median Absolute Deviation (MAD)802
Skewness3.700577531
Sum2840442
Variance38967572.64
MonotonicityNot monotonic
2022-10-12T14:25:47.751196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30726
 
3.1%
287620
 
2.4%
30920
 
2.4%
67415
 
1.8%
287515
 
1.8%
102413
 
1.6%
287712
 
1.4%
1711
 
1.3%
100911
 
1.3%
64711
 
1.3%
Other values (434)678
81.5%
ValueCountFrequency (%)
02
 
0.2%
11
 
0.1%
25
0.6%
31
 
0.1%
42
 
0.2%
53
 
0.4%
62
 
0.2%
91
 
0.1%
161
 
0.1%
1711
1.3%
ValueCountFrequency (%)
573801
0.1%
529181
0.1%
430351
0.1%
378631
0.1%
350861
0.1%
335511
0.1%
318511
0.1%
293781
0.1%
288041
0.1%
287591
0.1%

add
Real number (ℝ≥0)

HIGH CORRELATION

Distinct507
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18976.33894
Minimum0
Maximum572369
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:47.883225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile199.55
Q1881
median3548.5
Q319287
95-th percentile69201
Maximum572369
Range572369
Interquartile range (IQR)18406

Descriptive statistics

Standard deviation51470.9091
Coefficient of variation (CV)2.712372985
Kurtosis45.40232071
Mean18976.33894
Median Absolute Deviation (MAD)3338.5
Skewness6.116891397
Sum15788314
Variance2649254484
MonotonicityNot monotonic
2022-10-12T14:25:47.978246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20323
 
2.8%
25815
 
1.8%
21813
 
1.6%
20112
 
1.4%
1928811
 
1.3%
93710
 
1.2%
6772210
 
1.2%
45069
 
1.1%
2109
 
1.1%
194159
 
1.1%
Other values (497)711
85.5%
ValueCountFrequency (%)
01
 
0.1%
14
0.5%
81
 
0.1%
453
0.4%
461
 
0.1%
471
 
0.1%
691
 
0.1%
861
 
0.1%
1122
0.2%
1242
0.2%
ValueCountFrequency (%)
5723691
 
0.1%
4880741
 
0.1%
4511341
 
0.1%
4493101
 
0.1%
3736301
 
0.1%
3174261
 
0.1%
3048973
0.4%
2805861
 
0.1%
2705931
 
0.1%
2683751
 
0.1%

cmp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct474
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5492.820913
Minimum0
Maximum150206
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:48.078269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.75
Q1604.5
median2709.5
Q34011.25
95-th percentile24310
Maximum150206
Range150206
Interquartile range (IQR)3406.75

Descriptive statistics

Standard deviation10823.81014
Coefficient of variation (CV)1.9705376
Kurtosis50.17657811
Mean5492.820913
Median Absolute Deviation (MAD)2052.5
Skewness5.64233878
Sum4570027
Variance117154865.9
MonotonicityNot monotonic
2022-10-12T14:25:48.174290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55523
 
2.8%
1416
 
1.9%
93315
 
1.8%
378114
 
1.7%
378014
 
1.7%
377914
 
1.7%
312013
 
1.6%
57413
 
1.6%
57612
 
1.4%
256011
 
1.3%
Other values (464)687
82.6%
ValueCountFrequency (%)
01
 
0.1%
61
 
0.1%
134
 
0.5%
1416
1.9%
151
 
0.1%
194
 
0.5%
201
 
0.1%
241
 
0.1%
251
 
0.1%
263
 
0.4%
ValueCountFrequency (%)
1502061
0.1%
861381
0.1%
855271
0.1%
669531
0.1%
649731
0.1%
577921
0.1%
525091
0.1%
523601
0.1%
521821
0.1%
444981
0.1%

test
Real number (ℝ≥0)

HIGH CORRELATION

Distinct472
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3359.362981
Minimum0
Maximum45054
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:48.275313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1281
median1422.5
Q32260.25
95-th percentile16437.75
Maximum45054
Range45054
Interquartile range (IQR)1979.25

Descriptive statistics

Standard deviation6365.11565
Coefficient of variation (CV)1.894738879
Kurtosis13.42536005
Mean3359.362981
Median Absolute Deviation (MAD)1126
Skewness3.458959255
Sum2794990
Variance40514697.23
MonotonicityNot monotonic
2022-10-12T14:25:48.368334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21918
 
2.2%
135216
 
1.9%
154215
 
1.8%
153815
 
1.8%
153911
 
1.3%
154310
 
1.2%
139510
 
1.2%
15409
 
1.1%
17839
 
1.1%
5069
 
1.1%
Other values (462)710
85.3%
ValueCountFrequency (%)
01
 
0.1%
15
0.6%
31
 
0.1%
42
 
0.2%
59
1.1%
66
0.7%
74
0.5%
84
0.5%
93
 
0.4%
102
 
0.2%
ValueCountFrequency (%)
450541
0.1%
432831
0.1%
425321
0.1%
399461
0.1%
372311
0.1%
371481
0.1%
351651
0.1%
343071
0.1%
330861
0.1%
315191
0.1%

lea
Real number (ℝ≥0)

HIGH CORRELATION

Distinct434
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5089.272837
Minimum0
Maximum97763
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:48.568379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.65
Q1273
median665
Q32730
95-th percentile29261.5
Maximum97763
Range97763
Interquartile range (IQR)2457

Descriptive statistics

Standard deviation12233.41539
Coefficient of variation (CV)2.403764896
Kurtosis16.28246761
Mean5089.272837
Median Absolute Deviation (MAD)459
Skewness3.818712991
Sum4234275
Variance149656452.1
MonotonicityNot monotonic
2022-10-12T14:25:48.666401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19123
 
2.8%
64420
 
2.4%
64318
 
2.2%
99116
 
1.9%
55816
 
1.9%
99215
 
1.8%
64515
 
1.8%
23014
 
1.7%
21913
 
1.6%
66513
 
1.6%
Other values (424)669
80.4%
ValueCountFrequency (%)
01
 
0.1%
21
 
0.1%
43
 
0.4%
51
 
0.1%
611
1.3%
711
1.3%
81
 
0.1%
93
 
0.4%
111
 
0.1%
136
0.7%
ValueCountFrequency (%)
977631
0.1%
857821
0.1%
832301
0.1%
718151
0.1%
697421
0.1%
690731
0.1%
642411
0.1%
626441
0.1%
615351
0.1%
609621
0.1%

pop
Real number (ℝ≥0)

HIGH CORRELATION

Distinct520
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6983.0625
Minimum0
Maximum144940
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:48.764423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100.7
Q1468.5
median3919
Q35239.25
95-th percentile28314.65
Maximum144940
Range144940
Interquartile range (IQR)4770.75

Descriptive statistics

Standard deviation13988.78211
Coefficient of variation (CV)2.003244581
Kurtosis30.88775173
Mean6983.0625
Median Absolute Deviation (MAD)2938
Skewness4.792281118
Sum5809908
Variance195686025
MonotonicityNot monotonic
2022-10-12T14:25:48.860445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18723
 
2.8%
406813
 
1.6%
392310
 
1.2%
392510
 
1.2%
24510
 
1.2%
90549
 
1.1%
40748
 
1.0%
57868
 
1.0%
14368
 
1.0%
39218
 
1.0%
Other values (510)725
87.1%
ValueCountFrequency (%)
01
 
0.1%
44
0.5%
131
 
0.1%
201
 
0.1%
241
 
0.1%
271
 
0.1%
295
0.6%
302
 
0.2%
312
 
0.2%
332
 
0.2%
ValueCountFrequency (%)
1449401
0.1%
1425461
0.1%
948531
0.1%
897201
0.1%
812451
0.1%
805411
0.1%
800691
0.1%
798261
0.1%
722281
0.1%
708881
0.1%

FindFirstFile
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct197
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean427.5048077
Minimum0
Maximum57007
Zeros80
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:48.962468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median36
Q3263.25
95-th percentile659.95
Maximum57007
Range57007
Interquartile range (IQR)260.25

Descriptive statistics

Standard deviation2338.203311
Coefficient of variation (CV)5.469419919
Kurtosis416.1701585
Mean427.5048077
Median Absolute Deviation (MAD)36
Skewness18.03840222
Sum355684
Variance5467194.722
MonotonicityNot monotonic
2022-10-12T14:25:49.057489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
287
 
10.5%
080
 
9.6%
641
 
4.9%
437
 
4.4%
28434
 
4.1%
23627
 
3.2%
323
 
2.8%
122
 
2.6%
28219
 
2.3%
27716
 
1.9%
Other values (187)446
53.6%
ValueCountFrequency (%)
080
9.6%
122
 
2.6%
287
10.5%
323
 
2.8%
437
4.4%
515
 
1.8%
641
4.9%
75
 
0.6%
88
 
1.0%
98
 
1.0%
ValueCountFrequency (%)
570071
 
0.1%
104501
 
0.1%
104491
 
0.1%
104451
 
0.1%
104443
0.4%
93771
 
0.1%
90181
 
0.1%
88641
 
0.1%
88511
 
0.1%
62721
 
0.1%

SearchPathW
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct85
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.8461538
Minimum0
Maximum8197
Zeros276
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:49.160512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q313
95-th percentile109.95
Maximum8197
Range8197
Interquartile range (IQR)13

Descriptive statistics

Standard deviation1013.810776
Coefficient of variation (CV)6.076320925
Kurtosis51.2885878
Mean166.8461538
Median Absolute Deviation (MAD)6
Skewness7.168772347
Sum138816
Variance1027812.289
MonotonicityNot monotonic
2022-10-12T14:25:49.253532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0276
33.2%
359
 
7.1%
1653
 
6.4%
752
 
6.2%
1340
 
4.8%
1036
 
4.3%
929
 
3.5%
421
 
2.5%
520
 
2.4%
220
 
2.4%
Other values (75)226
27.2%
ValueCountFrequency (%)
0276
33.2%
117
 
2.0%
220
 
2.4%
359
 
7.1%
421
 
2.5%
520
 
2.4%
618
 
2.2%
752
 
6.2%
820
 
2.4%
929
 
3.5%
ValueCountFrequency (%)
81971
 
0.1%
81791
 
0.1%
81748
1.0%
70281
 
0.1%
57261
 
0.1%
57241
 
0.1%
57191
 
0.1%
57021
 
0.1%
35941
 
0.1%
10535
0.6%

SetFilePointer
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct328
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.2932692
Minimum0
Maximum81128
Zeros120
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:49.353555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median48
Q3350.75
95-th percentile886
Maximum81128
Range81128
Interquartile range (IQR)348.75

Descriptive statistics

Standard deviation2955.254652
Coefficient of variation (CV)8.090087885
Kurtosis675.5672766
Mean365.2932692
Median Absolute Deviation (MAD)48
Skewness25.04170858
Sum303924
Variance8733530.058
MonotonicityNot monotonic
2022-10-12T14:25:49.454578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120
 
14.4%
268
 
8.2%
128
 
3.4%
427
 
3.2%
1222
 
2.6%
2119
 
2.3%
318
 
2.2%
617
 
2.0%
1014
 
1.7%
698
 
1.0%
Other values (318)491
59.0%
ValueCountFrequency (%)
0120
14.4%
128
 
3.4%
268
8.2%
318
 
2.2%
427
 
3.2%
53
 
0.4%
617
 
2.0%
73
 
0.4%
82
 
0.2%
92
 
0.2%
ValueCountFrequency (%)
811281
0.1%
199961
0.1%
74031
0.1%
68001
0.1%
66351
0.1%
65261
0.1%
51451
0.1%
48191
0.1%
42571
0.1%
38791
0.1%

FindResourceEx
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct247
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.8834135
Minimum0
Maximum5777
Zeros196
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:49.560602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median70
Q3597
95-th percentile674.35
Maximum5777
Range5777
Interquartile range (IQR)596

Descriptive statistics

Standard deviation495.2689963
Coefficient of variation (CV)1.577875654
Kurtosis54.17686219
Mean313.8834135
Median Absolute Deviation (MAD)70
Skewness5.734657828
Sum261151
Variance245291.3787
MonotonicityNot monotonic
2022-10-12T14:25:49.653622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0196
23.6%
340
 
4.8%
226
 
3.1%
62223
 
2.8%
59620
 
2.4%
60018
 
2.2%
58716
 
1.9%
116
 
1.9%
1114
 
1.7%
513
 
1.6%
Other values (237)450
54.1%
ValueCountFrequency (%)
0196
23.6%
116
 
1.9%
226
 
3.1%
340
 
4.8%
42
 
0.2%
513
 
1.6%
62
 
0.2%
710
 
1.2%
84
 
0.5%
97
 
0.8%
ValueCountFrequency (%)
57772
0.2%
50051
0.1%
49981
0.1%
25061
0.1%
24871
0.1%
21181
0.1%
20441
0.1%
17781
0.1%
17561
0.1%
15091
0.1%

GetFileAttributesW
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct290
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.6622596
Minimum0
Maximum14264
Zeros105
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:49.752645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median64
Q3762
95-th percentile858
Maximum14264
Range14264
Interquartile range (IQR)760

Descriptive statistics

Standard deviation717.1497452
Coefficient of variation (CV)1.972021364
Kurtosis217.7724827
Mean363.6622596
Median Absolute Deviation (MAD)64
Skewness12.37827919
Sum302567
Variance514303.757
MonotonicityNot monotonic
2022-10-12T14:25:49.843665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105
 
12.6%
288
 
10.6%
139
 
4.7%
812
 
1.4%
512
 
1.4%
311
 
1.3%
76410
 
1.2%
229
 
1.1%
49
 
1.1%
7618
 
1.0%
Other values (280)529
63.6%
ValueCountFrequency (%)
0105
12.6%
139
 
4.7%
288
10.6%
311
 
1.3%
49
 
1.1%
512
 
1.4%
68
 
1.0%
77
 
0.8%
812
 
1.4%
92
 
0.2%
ValueCountFrequency (%)
142641
0.1%
105411
0.1%
34181
0.1%
16591
0.1%
15381
0.1%
15211
0.1%
14751
0.1%
14531
0.1%
14001
0.1%
13691
0.1%

SetFileAttributesW
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct99
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.69110577
Minimum0
Maximum977
Zeros290
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:49.945688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3234
95-th percentile287
Maximum977
Range977
Interquartile range (IQR)234

Descriptive statistics

Standard deviation123.4153057
Coefficient of variation (CV)1.360831414
Kurtosis2.004026241
Mean90.69110577
Median Absolute Deviation (MAD)3
Skewness1.117112331
Sum75455
Variance15231.33768
MonotonicityNot monotonic
2022-10-12T14:25:50.044710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0290
34.9%
160
 
7.2%
354
 
6.5%
439
 
4.7%
236
 
4.3%
23623
 
2.8%
23315
 
1.8%
26812
 
1.4%
28912
 
1.4%
23711
 
1.3%
Other values (89)280
33.7%
ValueCountFrequency (%)
0290
34.9%
160
 
7.2%
236
 
4.3%
354
 
6.5%
439
 
4.7%
57
 
0.8%
611
 
1.3%
77
 
0.8%
85
 
0.6%
92
 
0.2%
ValueCountFrequency (%)
9771
 
0.1%
6381
 
0.1%
4041
 
0.1%
3492
 
0.2%
2991
 
0.1%
2925
0.6%
2911
 
0.1%
2905
0.6%
28912
1.4%
28810
1.2%

SetFilePointerEx
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct209
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.24038462
Minimum0
Maximum7387
Zeros316
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:50.250756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3110
95-th percentile347.7
Maximum7387
Range7387
Interquartile range (IQR)110

Descriptive statistics

Standard deviation386.0803834
Coefficient of variation (CV)4.05374658
Kurtosis207.4969687
Mean95.24038462
Median Absolute Deviation (MAD)2
Skewness13.19461058
Sum79240
Variance149058.0625
MonotonicityNot monotonic
2022-10-12T14:25:50.344777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0316
38.0%
293
 
11.2%
148
 
5.8%
334
 
4.1%
512
 
1.4%
805
 
0.6%
2395
 
0.6%
1385
 
0.6%
3515
 
0.6%
594
 
0.5%
Other values (199)305
36.7%
ValueCountFrequency (%)
0316
38.0%
148
 
5.8%
293
 
11.2%
334
 
4.1%
43
 
0.4%
512
 
1.4%
63
 
0.4%
73
 
0.4%
81
 
0.1%
93
 
0.4%
ValueCountFrequency (%)
73871
0.1%
50771
0.1%
41891
0.1%
32251
0.1%
24471
0.1%
17661
0.1%
7371
0.1%
5231
0.1%
4791
0.1%
4701
0.1%

CryptEncrypt
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct162
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.90985577
Minimum0
Maximum3874
Zeros452
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:50.446800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q349
95-th percentile205
Maximum3874
Range3874
Interquartile range (IQR)49

Descriptive statistics

Standard deviation212.9783926
Coefficient of variation (CV)4.102850787
Kurtosis210.4825611
Mean51.90985577
Median Absolute Deviation (MAD)0
Skewness13.60254871
Sum43189
Variance45359.79572
MonotonicityNot monotonic
2022-10-12T14:25:50.544822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0452
54.3%
450
 
6.0%
522
 
2.6%
1437
 
0.8%
306
 
0.7%
376
 
0.7%
495
 
0.6%
1015
 
0.6%
1505
 
0.6%
684
 
0.5%
Other values (152)270
32.5%
ValueCountFrequency (%)
0452
54.3%
14
 
0.5%
23
 
0.4%
34
 
0.5%
450
 
6.0%
522
 
2.6%
61
 
0.1%
72
 
0.2%
93
 
0.4%
122
 
0.2%
ValueCountFrequency (%)
38741
0.1%
30041
0.1%
29221
0.1%
15551
0.1%
4441
0.1%
3521
0.1%
3161
0.1%
3141
0.1%
3121
0.1%
2831
0.1%

CreateThread
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct56
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53485577
Minimum0
Maximum94
Zeros124
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:50.648845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q328
95-th percentile38
Maximum94
Range94
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.6773969
Coefficient of variation (CV)1.009806849
Kurtosis1.271796225
Mean14.53485577
Median Absolute Deviation (MAD)8
Skewness1.025027733
Sum12093
Variance215.4259798
MonotonicityNot monotonic
2022-10-12T14:25:50.746868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124
14.9%
271
 
8.5%
3055
 
6.6%
555
 
6.6%
2853
 
6.4%
347
 
5.6%
139
 
4.7%
435
 
4.2%
628
 
3.4%
1124
 
2.9%
Other values (46)301
36.2%
ValueCountFrequency (%)
0124
14.9%
139
 
4.7%
271
8.5%
347
 
5.6%
435
 
4.2%
555
6.6%
628
 
3.4%
714
 
1.7%
820
 
2.4%
913
 
1.6%
ValueCountFrequency (%)
941
0.1%
831
0.1%
781
0.1%
771
0.1%
761
0.1%
531
0.1%
521
0.1%
511
0.1%
501
0.1%
491
0.1%

FindResourceExW
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct241
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.5192308
Minimum0
Maximum5777
Zeros195
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-10-12T14:25:50.844889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median70
Q3597
95-th percentile674.35
Maximum5777
Range5777
Interquartile range (IQR)596

Descriptive statistics

Standard deviation495.2433571
Coefficient of variation (CV)1.579626729
Kurtosis54.20457674
Mean313.5192308
Median Absolute Deviation (MAD)70
Skewness5.737019005
Sum260848
Variance245265.9828
MonotonicityNot monotonic
2022-10-12T14:25:50.939911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0195
23.4%
342
 
5.0%
227
 
3.2%
62223
 
2.8%
59620
 
2.4%
60018
 
2.2%
116
 
1.9%
58715
 
1.8%
1113
 
1.6%
61212
 
1.4%
Other values (231)451
54.2%
ValueCountFrequency (%)
0195
23.4%
116
 
1.9%
227
 
3.2%
342
 
5.0%
42
 
0.2%
512
 
1.4%
62
 
0.2%
79
 
1.1%
86
 
0.7%
97
 
0.8%
ValueCountFrequency (%)
57772
0.2%
50051
0.1%
49981
0.1%
25061
0.1%
24871
0.1%
21181
0.1%
20421
0.1%
17781
0.1%
17561
0.1%
15091
0.1%

Cerber
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
0
443 
1
389 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters832
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Length

2022-10-12T14:25:51.028931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-12T14:25:51.124952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Most occurring characters

ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number832
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0443
53.2%
1389
46.8%

Interactions

2022-10-12T14:25:44.543477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:08.667458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:10.454869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:12.278268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:14.472760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:16.437200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:18.578681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:20.732986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:22.718581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:24.480976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:26.296385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:28.192810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:30.022220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:31.690594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:33.603023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:35.459449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:37.327858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:39.038241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:40.912662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:42.794084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:43.701288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:45.575708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:09.771706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:11.589113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:13.412523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:15.668028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:17.756496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:19.907980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:21.823164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:23.808826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:29.209037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:31.017443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:40.197502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:43.777305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:45.667729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:09.862736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:11.679134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:13.521547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:15.770051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:21.919123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:29.300058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:25.785269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:29.387078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-12T14:25:42.252963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-12T14:25:43.948343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-12T14:25:51.390011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-12T14:25:51.577054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-12T14:25:51.758094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-10-12T14:25:46.613941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

pushmovcallsubjmpaddcmptestleapopFindFirstFileSearchPathWSetFilePointerFindResourceExGetFileAttributesWSetFileAttributesWSetFilePointerExCryptEncryptCreateThreadFindResourceExWCerber
07610310131329344485912795338261369418741110521988110101100000
12317533515743816743228933514681147920212650000260
225822874112390335357163437428779433371051631910000000000
3760941152964697259251229475752064613479214331681611111000110
425073906742394636057201429508541422471491588100000000000
51397512057956985890112349128419294972168751881271344448223105244523203810510
64886899241573456527254896713832952314109498110101000000
738761712612115833178707994656678593289932559220101000100
810621169551467771242910500152891366917466302951299700101000100
92474641079160582560591348087936507392757365375547607261220166070

Last rows

pushmovcallsubjmpaddcmptestleapopFindFirstFileSearchPathWSetFilePointerFindResourceExGetFileAttributesWSetFileAttributesWSetFilePointerExCryptEncryptCreateThreadFindResourceExWCerber
8222123146291593304200563233214197195237403315213738741031
823518594541128265128751941637811543991393327716636227602235836306221
824517493911126264828721929437781540643392127716586227612235233306221
8259155108606572959106945067149125385690542481031564256311
8265422901192924911114410827121461545411650310195621788251184112306211
82769971412711043040126536093307178469476122647207607767248183111286071
828699714127110430401265360933071784694761227010223620771251200121286201
829517693911128264728741928937801538644392327520226807552181713286801
8305179944711272652287719413378015429913925277161162275221767306221
8312065143190995307203555220191187227744458766023611068185871

Duplicate rows

Most frequently occurring

pushmovcallsubjmpaddcmptestleapopFindFirstFileSearchPathWSetFilePointerFindResourceExGetFileAttributesWSetFileAttributesWSetFilePointerExCryptEncryptCreateThreadFindResourceExWCerber# duplicates
011140037162016014672920211200021113
12813694854402384510593511584284202000200003
9266972274784251649424221094815572039115786223162801131380118003
104827115827127228150110030489770312521132237089600020000003
215333568841219298522709281610586000000000002
32187155993696317210576237230243361247830054702
42191157493797319201576238230245331211830031102
522121650934119308201605260241245333973003902
6258316871100102357258598297276351233963003902
725831687110010235725859829727635126345630054502